The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
Traditional methods for studying visitors' behavior at a given place-of-interest (e.g., businesses, schools, town halls, distinctive buildings, post offices, restaurants, tourist attractions, etc.), such as surveys and manual collection, are expensive, time-consuming and cannot be easily applied at a large scale. However, in recent years, data from personal devices with location sensing capabilities, such as smartphones and wearable devices, have emerged as new sources for studying visitors' behavior. The location-specific data from visitor personal devices can be exploited by data mining algorithms to discover knowledge patterns that may be helpful for smarter planning in long-term business strategy and development. The patterns can be used to have a better understanding of the visitor traffic over time at a particular place-of-interest. The patterns may also help to pinpoint the best location for a particular brand of business, study land or rental prices of commercial and residential locations, and provide location-specific advertising. The knowledge patterns will also allow urban planners and developers the ability to find solutions for problems that require analyzing location-specific data, such as, the ideal street for a bike-share location, or reconfiguring roadways for optimal traffic flow.
A place-of-interest is generally associated with a centroid that specifies, at a minimum, geospatial coordinates of the place-of-interest (i.e., the latitude and the longitude). However, the data gathered by relying on centroids has low accuracy. The circular mapping of the centroid may not represent the actual perimeter of the place-of-interest. If one or more places-of-interest are nearby, such as multiple places-of-interest in one building structure, the circular mappings of their centroids may overlap. The overlapping may result in noisy location-specific data. Furthermore, a centroid of a place-of-interest may not be at the center of the location, and the circular mapping of the centroid may overlap with visitor traffic outside the perimeter of the place-of-interest.
An opportunity arises to leverage places-of-interest that are currently available, improve their centroid so that the improved centroid represents the center of the location, and define their physical perimeter in buildings that may be shared by multiples places-of-interest.